Introduction: Gliomas are the most common primary malignant brain tumours with a very poor survival. Resistance to chemotherapy and radiotherapy often occurs in these tumours due to hypoxia, which can be caused by different oxygenation parameters. Multiple magnetic resonance imaging (MRI) techniques are able to assess these oxygenation parameters. Since multiple artificial intelligence (AI) approaches exist to fuse these oxygenation images together, the goal of this research is to find and implement an AI approach to generate a combined representation of multiple oxygenation parameters acquired by MRI so hypoxia within brain tumours can be detected and located. Literature study: Multiple MR imaging techniques that are able to measure different oxygenation parameters have been reviewed in the literature study. In addition, an overview was given of different AI approaches for combining the acquired information of the discussed MR imaging techniques. Based on the literature findings, hierarchical clustering was the most promising AI approach for this research purpose. Methods: A specific MR imaging protocol was designed to assess information regarding tumour oxygenation within patients with brain tumours. After registration of the acquired images to the same space, the voxels of healthy brain tissues in these images were used for performing hierarchical clustering multiple times to identify the optimal parameter settings of the algorithm. Then the clustering was performed multiple times with different data types in order to achieve a decrease of required computational power. After segmentation of the tumour area in the MR images, the clustering was applied to the tumour voxels to generate a spatial map of the tumour showing the location of the clusters representing different states of oxygenation. Evaluation of the clusters was performed by visualizing the distribution of the oxygenation parameter values within the different clusters. Results: Three patients were included and underwent MR imaging. Results showed that ward linkage and Euclidean distance resulted in the highest clustering performance when performing hierarchical clustering on data of healthy brain tissue. Changing the data type of input data did not lead to a decrease in required computational power. Applying the hierarchical clustering with the optimal parameter settings on tumour voxels resulted in spatial maps of the different clusters within the tumour. Evaluation of the distribution of the oxygenation parameter values showed differences among different clusters within the patients. However, within two patients the number of clusters present within the tumour, changed when including different MR images in the clustering analysis. Conclusion: This research showed that hierarchical clustering is an AI approach which is able to identify clusters with a different distribution of oxygenation parameter values acquired by MR imaging. Visualizing these different clusters in a spatial map results in a combined representation of these oxygenation parameters. Despite the promising results, future work is needed to investigate other clustering methods regarding this research purpose, the importance of each individual MR technique, and a validation method of what type of oxygenation the clusters represent